A single-loop bilevel deep learning method for optimal control of obstacle problems

Optimal control of obstacle problems arises in a wide range of applications and is computationally challenging due to the nonsmoothness, nonlinearity, and bilevel structure of the underlying problems. Classical numerical methods typically rely on mesh-based discretizations and require solving a sequence of computationally expensive subproblems, while deep learning approaches for this class of problems remain largely unexplored. In this work, we propose a bilevel deep learning framework whose core training algorithm is the computationally efficient Single-Loop Stochastic First-Order Bilevel Algorithm (S2-FOBA), which avoids nested optimization, uses only first-order information, and does not rely on restrictive lower-level uniqueness assumptions. The proposed method is mesh-free, scalable to high-dimensional settings and complex domains, and avoids the repeated solution of discretized subproblems. It employs constraint-embedding neural networks to approximate both the state and the control, thereby enforcing admissibility by construction while preserving the intrinsic bilevel structure of the problem. We further analyze the convergence behavior of S2-FOBA under mild conditions. Numerical experiments on benchmark problems, including two-dimensional examples with regular and irregular obstacles, a five-dimensional example, and a problem posed on a complex domain, demonstrate the effectiveness, efficiency, and robust constraint enforcement of the proposed approach.